Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data

Wang, Huixia; Huang, Shuguang; Shou, Jianyong; Su, Eric W.; Onyia, Jude E.; Liao, Birong; Li, Shuyu

doi:10.1186/1471-2164-7-166

Cited by 48 publications

(40 citation statements)

References 27 publications

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“…While genomic analyses of mammalian tumor-derived cell lines (e.g. Wang et al 2006) have confirmed that the cells often retain global similarities to their tissues of origin, those studies have not pursued the cell-by-cell distinctions made possible here by comparison to a large developmental literature on imaginal discs.…”

Section: Discussionmentioning

confidence: 94%

The transcriptional diversity of 25 Drosophila cell lines

Cherbas¹,

Willingham²,

Zhang³

et al. 2010

Genome Res.

248

311

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Drosophila melanogaster cell lines are important resources for cell biologists. Here, we catalog the expression of exons, genes, and unannotated transcriptional signals for 25 lines. Unannotated transcription is substantial (typically 19% of euchromatic signal). Conservatively, we identify 1405 novel transcribed regions; 684 of these appear to be new exons of neighboring, often distant, genes. Sixty-four percent of genes are expressed detectably in at least one line, but only 21% are detected in all lines. Each cell line expresses, on average, 5885 genes, including a common set of 3109. Expression levels vary over several orders of magnitude. Major signaling pathways are well represented: most differentiation pathways are “off” and survival/growth pathways “on.” Roughly 50% of the genes expressed by each line are not part of the common set, and these show considerable individuality. Thirty-one percent are expressed at a higher level in at least one cell line than in any single developmental stage, suggesting that each line is enriched for genes characteristic of small sets of cells. Most remarkable is that imaginal disc-derived lines can generally be assigned, on the basis of expression, to small territories within developing discs. These mappings reveal unexpected stability of even fine-grained spatial determination. No two cell lines show identical transcription factor expression. We conclude that each line has retained features of an individual founder cell superimposed on a common “cell line“ gene expression pattern.

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Section: Discussionmentioning

confidence: 94%

The transcriptional diversity of 25 Drosophila cell lines

Cherbas¹,

Willingham²,

Zhang³

et al. 2010

Genome Res.

248

311

View full text Add to dashboard Cite

show abstract

“…(33) but is modified in three important ways. The first modification is that we use Spearman's rank correlation rather than Pearson product moment correlation to measure gene expression similarity.…”

Section: Resultsmentioning

confidence: 99%

“…Our choice of a rank-based classifier is motivated by the fact that the rank value of a probe’s expression level within a sample is insensitive to standard within-sample preprocessing methods and is not directly affected by other probes or samples on the array, whereas the expression value is highly dependent on the pre-processing and normalization methods used and can be affected by additional probes and samples (e.g., when robust multichip average (RMA) normalization is used). The second modification is that instead of measuring similarity based on a sample’s global gene expression profile (i.e., the expression levels of all common microarray probes) as used previously (33), we measure similarity based on gene signatures appropriate for the microarray data sets under study and the phenotype of interest (Figure 1C). For tissue alignment, because bladder samples (N=350) were both formalin fixed, paraffin embedded (FFPE) and fresh frozen (FF) (Supplementary Table S2), the gene signature used consists of 12,402 probes whose expression values were preserved across FF and FFPE (24).…”

Section: Resultsmentioning

confidence: 99%

A Framework to Select Clinically Relevant Cancer Cell Lines for Investigation by Establishing Their Molecular Similarity with Primary Human Cancers

Dancik

Owens

et al. 2011

Cancer Research

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Experimental work using human cancer cell lines often does not translate to the clinic. We posit this is because some cells undergo changes in vitro that no longer make them representative of human tumors. Here we describe a novel alignment method named SRCCM (Spearman’s Rank Correlation Classification Method) that measures similarity between cancer cell lines and human tumors via gene expression profiles, for the purpose of selecting lines that are biologically relevant. To demonstrate utility, we used SRCCM to assess similarity of 36 bladder cancer lines with 10 epithelial human tumor types (N=1630 samples) and with bladder tumors of different stages and grades (N=144 samples). While 34/36 lines aligned to bladder tumors rather than other histologies, only 16/28 had SRCCM assigned grades identical to that of their original source tumors. To evaluate the clinical relevance of this approach, we demonstrate that gene expression profiles of aligned cell lines stratify survival in an independent cohort of 87 bladder patients (HR = 3.41, logrank p = 0.0077), while unaligned cell lines using original tumor grades did not. We repeated this process on 22 colorectal cell lines and found that gene expression profiles of 17 lines aligning to colorectal tumors and selected based on their similarity with 55 human tumors stratified survival in an independent cohort of 177 colorectal cancer patients (HR = 2.35, logrank p = 0.0019). By selecting cell lines that reflect human tumors, our technique promises to improve the clinical translation of laboratory investigations in cancer.

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“…In a comparison of publicly available TCGA kidney tumours with CCLP and CCLE data for kidney cell lines, we have sub-classified commercially available RCC cell lines into their likely respective RCC sub-histologies; clear cell or papillary (none of the cell lines matched the chromophobe subtype). Previous studies have confirmed that certain RCC cell lines are truly derived from kidney tumours536 and others have categorized them into generic subgroups based on molecular signatures37. However, none of the previous analyses investigated which particular RCC subtype these cell lines originate from.…”

Section: Discussionmentioning

confidence: 99%

Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection

et al. 2017

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The utility of cancer cell lines is affected by the similarity to endogenous tumour cells. Here we compare genomic data from 65 kidney-derived cell lines from the Cancer Cell Line Encyclopedia and the COSMIC Cell Lines Project to three renal cancer subtypes from The Cancer Genome Atlas: clear cell renal cell carcinoma (ccRCC, also known as kidney renal clear cell carcinoma), papillary (pRCC, also known as kidney papillary) and chromophobe (chRCC, also known as kidney chromophobe) renal cell carcinoma. Clustering copy number alterations shows that most cell lines resemble ccRCC, a few (including some often used as models of ccRCC) resemble pRCC, and none resemble chRCC. Human ccRCC tumours clustering with cell lines display clinical and genomic features of more aggressive disease, suggesting that cell lines best represent aggressive tumours. We stratify mutations and copy number alterations for important kidney cancer genes by the consistency between databases, and classify cell lines into established gene expression-based indolent and aggressive subtypes. Our results could aid investigators in analysing appropriate renal cancer cell lines.

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Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data

Cited by 48 publications

References 27 publications

The transcriptional diversity of 25 Drosophila cell lines

The transcriptional diversity of 25 Drosophila cell lines

A Framework to Select Clinically Relevant Cancer Cell Lines for Investigation by Establishing Their Molecular Similarity with Primary Human Cancers

Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection

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